STATEOF

REPORT

2025

TheStateof

Self-Service

andAutomation

ByFernHalper,Ph.D.

TDWIVPofResearch

Sponsoredby:

precisely

dwv

StateofSelf-ServiceandAutomation

ByFernHalper,Ph.D.

TableofContents

TheScopeandImportanceofSelf-Service 2

TheOverallStateofSelf-ServiceMaturity 4

TheStateofOrganizationalCultureforSelf-Service 5

TheStateofDataMaturityforSelf-Service 7

TheStateofDataInfrastructureMaturityforSelf-Service 8

TheStateofSelf-ServiceEnablement 11

BuildingDataLiteracy 13

TheStateofGovernanceforSelf-Service 13

ConsiderationsandBestPracticesforIncreasing

Self-ServiceMaturity 14

ResearchMethodology 17

FromOurSponsor 18

AbouttheAuthor 19

AboutTDWIResearch 19

?2025byTDWI,adivisionof1105Media,Inc.Allrightsreserved.Reproductionsinwholeorpartare

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organizationorvalidationofitsclaims.ThisreportisbasedonindependentresearchandrepresentsTDWI’sfindings;readerexperiencemaydiffer.Theinformationcontainedinthisreportwasobtainedfromsourcesbelievedtobereliableatthetimeofpublication.Featuresandspecificationscananddochangefrequently;readersareencouragedtovisitvendorwebsitesforupdatedinformation.TDWIshallnotbeliableforany

omissionsorerrorsintheinformationinthisreport.

1

StateofSelf-ServiceandAutomation2

TheScopeandImportanceofSelf-Service

Whenself-servicefirstgainedtraction,

thefocuswaslargelyonanalytics,giving

businessuserseasieraccesstodataand

visualizationtools.Today,self-service

extendsacrosstheentiredataandanalyticslifecycle.Automationiscentraltothis

shift,withlow-codeandno-codeplatformsenablingcitizendeveloperstocreateand

managedataprocesses,improvequality,andensureintegrity—allcriticaltoanalytics—

withoutheavyITinvolvement.Byexpandingself-service,organizationscanbuildatrustedfoundationthatempowersmorepeopletoworkconfidentlywithdata.

Organizationscontinuetoprioritizeself-

serviceanalyticsasawaytoaccelerate

decision-makingandreduceITbottlenecks.Thistrendisimportanttothose

organizationsthatwanttomovebeyond

spreadsheetsandstaticdashboardstobuildananalyticsculture.Thisisoneinwhich

insightsarenotconfinedtoacentralizedteam,butinsteadareembeddedin

everydayworkflowsacrossdepartments.

Self-servicecanreducebottlenecks,

acceleratetimetoinsight,andfostergreatercollaborationbetweenbusinessandIT,withITenablingandgoverningtheenvironmentinthebackground.

Today,self-serviceisevolvingbeyond

simpleaccessandvisualizationtoincludemoreadvancedcapabilities.Solutionsarenowemergingthatsurfaceautomated

insights,guideusersthroughtheprocessofbuildingpredictivemodels,andembedmachinelearningwithinintuitiveinterfaces.

GenerativeAIisfurtheraccelerating

thisshiftbyprovidingnaturallanguage

interfacesfordata—allowinguserstoaskquestions,exploretrends,andgenerate

summarieswithoutneedingdeeptechnicalskills.Theseinnovationslowerthebarriertoentryforadvancedanalyticsandmightclosethegapbetweendataaccessand

meaningful,self-serviceinsight.Infact,theintegrationofgenerativeAIintoBIandAItoolsmaybecometheforcethatpropels

self-servicetoenterprise-widecapability.Benefitsofself-serviceinclude:

?Greateragilityandcollaboration.

Extendingself-serviceacrossthedatalifecycle,supportedbyautomation

andlow-codetools,createsashared

understandingacrossdepartments,

aligninggoalsandimproving

communication.Organizationsthat

embracethisculturearemoreagileandbetterequippedtoadapttodynamicmarketconditions.

?Improveddecision-making.Enabling

businessuserstodirectlyaccessdata

andanalyticstoolsimprovestheirabilitytomakereal-time,data-drivendecisions.ThisremovesdependenciesonITor

centralizeddatateams,accelerating

insightgeneration.Self-servicemakes

sensesincebusinessusersareclosesttothequestionsthatmatter.Empowering

themtoaskfollow-upquestions,performexploratoryanalysis,andtesthypothesesleadstobetterdecisions.Forexample,

marketingprofessionalsmayneedto

quicklyassessandexperimentwith

customerexperiencemetrics;theyrequire

StateofSelf-ServiceandAutomation3

fastaccesstoanalyticstomaketimely

improvementsthatcan’talwayswaitforIT.

?Employeeempowerment.Accessto

self-serviceanalyticssupportscontinuouslearning,skilldevelopment,andgreater

employeeengagementacrosstechnical

andnon-technicalroles.InTDWIresearch,we’veseenthatrespondentsbelieve

empoweringmoreuserswithanalytics

capabilitiesisimportanttoincreasingdatavalueandBIsuccess.ITteamsalsobenefitbyshiftingfromrepetitivedashboard

creationtomorestrategic,advancedtaskssuchasmachinelearning.Businessusersinfunctionssuchassalesandmarketinggainautonomytoanalyzeandactondataindependently.Thiscreatesmutualbenefitandimprovestheoverallorganizational

dataculture.

Thisisn’ttosaythateveryoneinthe

organizationshouldanalyzetheirdata.

Someemployeesmaynothaveananalyticsmindset.Theymaybeoverburdenedwithotherresponsibilitiesorsimplydon’tsee

thevalueofusingdatafordecisionsin

theirparticularjob.Thegoalofself-serviceshouldbetoensurethatthosewhoneeddatatodrivestrategicoroperational

decisionsareempoweredtouseit.

ForyearsinTDWIresearch,we’veseenthatself-serviceanalyticsisatoppriorityfor

organizations.Yetmanycompaniesseemtostruggletodemocratizetheiranalyticsefforts.Aswewillsee,therecanbe

numerousreasonsforthis,and,ofcourse,self-servicecapabilitiescontinuetoevolve.This“StateOf”analysisillustrateswheretherearestillgapstodayandprovides

Morecollaboration

Greateragility

Betterdecisions

Empoweredemployees

BenefitsofSelf-service

StateofSelf-ServiceandAutomation4

somebestpracticesforwhatitwilltaketomoveforward.

TheOverallStateofSelf-ServiceMaturity

Thereareanumberofinterrelatedfactors

thatinformthecurrentstateofself-service.Self-serviceisnotsimplyamatterofusing

avisualanalyticstoolagainstadatasetor

evenusinggenerativeAItogetanswersfromdatainanaturallanguageway.Itinvolves

people,processes,andtechnologies.It

involvesbeingabletoaccessdatainaself-servicemanner,analyzeit,andtrustthedata.Itrequiresdataliteracy,i.e.,users’ability

tounderstandandinteractwithdataandanalyticsandcommunicatetheresultstoachievebusinessgoals.

Inthesurveyforthisreport,welookedatfivedifferentfactorsforself-servicesuccess:

organizationalculture,datamanagement

toolsandprocesses,infrastructure,analyticsenablement,andgovernancefordataand

analytics.Thesurveyanswersweremeasured

ona5-pointscalefromleastmaturetomostmaturepractices.

Theresultsindicatethatorganizationsareprogressingsteadilytowardenablingself-serviceanalyticscapabilities,withanoverallaveragematurityscoreof3.31.

Theareasinwhichthemostrespondents’practiceswerethemostmatureinclude

strongcollaborationbetweenbusinessandITteams(meanscoreof3.8),broadtool

access(3.7),activeleadershipsupport(3.7),andarelativelyhighlevelofusertrustin

thedata(3.7),allofwhichareimportantforsuccessfulself-serviceinitiatives.

However,thedataalsohighlightsseveralareasthatrequirefurtherdevelopment.

Notably,theactualadoptionofadvancedanalyticstoolsremainslimited(mean

2.8maturityscore),andonlyasmall

proportionofbusinessusersareregularlyengaginginself-serviceanalytics,whichcansuggestuntappedpotentialin

expandinguserenablement.Additionally,governance,particularlyaroundemerging

Dimension

AverageScore(Outof5)

Organizationalculture

3.5

Datamanagementtoolsandprocesses

3.4

Infrastructure

3.3

Enablinganalytics

3.2

DataandAIgovernance

3.1

Figure1.Thedimensionsofself-serviceanalyticsmaturityandparticipants’

averagescoresforeachdimension.

StateofSelf-ServiceandAutomation5

Ourorganization'sleadershipprovidesactive

funding,strategy,andsupportforbuildingtrusted,

self-servicedataandanalyticscapabilities.

Stronglydisagree3%

Disagree13%

Neutral23%

Agree37%

Stronglyagree24%

Figure2.Basedon215respondents.

technologiessuchasgenerativeAI,lagsbehind—potentiallyindicatingtheneedfororganizationstostrengthentheir

frameworkstosupportinnovationwhileensuringdatacomplianceandsecurity(2.8,withamedianof2).

Theseinsightssuggestamoderatelystrongfoundationwithclearopportunitiesto

enhancethereach,depth,andgovernanceofself-serviceanalyticsacrosstheenterprise,especiallyasitcontinuestoevolve.

Respondents’strengthslieinstrategicintentandculturalalignment.Weaknessesare

moretechnicalandstructuralintermsofgovernancerigor,tooldiversity,andevenmetadatainfrastructure.

TheStateofOrganizationalCultureforSelf-Service

Organizationalcultureiscriticalformovingforwardwithself-service.Buildingthat

cultureoftenrequiresleadershipthatactivelysupportsself-serviceandcollaboration

betweenbusinessandIT.Astrongculture

canresultinalargenumberofusersmakingdata-drivendecisionsandbusinessusers

engaginginself-serviceanalytics.

IntheStateofSelf-Servicesurvey,over

halfofrespondentsstatedthattheir

organization’sleadershipprovidesactive

funding,strategy,andsupportforbuildingtrusted,self-servicedataandanalytics

capabilities(Figure2).Welloverhalf(66%)notedthatnon-technicalusersaswellas

businessanalystsanddatascientistsmakeuseofself-serviceanalyticstools(asidefromspreadsheets)intheircompany(notshown).Thissuggeststheemergenceofstrong

culturalsupportforself-service.Infact,thisareaofself-servicematurityhastheoverallhighestaverageacrossthefiveareas,anditissignificantlyhigherthanthelowest

averageingovernance.Thatisgoodnews.

Yetwidespreadadoptionofself-service

appearstobelaggingdespiteleadership

support(Figure3).Forexample,themajorityofrespondentsreportthatlessthan50%

ofbusinessuserscurrentlymakeuseof

StateofSelf-ServiceandAutomation6

Whatpercentofbusinessuserscurrentlymakeuseofself-serviceanalyticsinyourorganization?

22%

Lessthan

24%

11–25%

23%

26–45%

10%

16%

14%

46–65%Greater

than65%

Figure3.Basedon215respondents.

self-serviceanalytics.(Asstatedearlier,not

allbusinessusersnecessarilyneedtouse

self-service.)Therelativelylowscoreon

thepercentageofbusinessusersactually

performingself-serviceanalyticshighlightsareadinessvs.executiongap.Thismaybea

resultofinsufficienttrainingorenablement,toolcomplexity,orlackoftrustorconfidenceamongbusinessusers.Itmaybethat

respondentsperceivetheircultureasstrongerthanitisforactuallysupportingself-service.

InotherTDWIresearch,we’veseenthatwhilecertainexecutivesmaychampiondata-drivendecision-making,it’snot

uniformacrossleadership.We’veseenthatsometimesorganizationshave

aBIstrategy,butitisnoteffectively

communicated—inthatcase,manypeopledon’tunderstandthattherearetoolsin

placethatcanhelpthemorprogramsthatcanhelpwithdataliteracy.Andsometimespeoplejustresistchange.

Forinstance,inourBIandAImaturitymodelassessment,1weasked,“WouldyousaythatyourBIisdemocratized(i.e.,thatbusiness

usersaremakinguseofit)?”Themajority

(closeto60%)statedthatsomebusiness

usersactivelyuseBI,butadoptionisuneven.Inthesurveyforthisreport,whilethe

respondentsaverageda3.3maturityscoreindataliteracy,thislaggedbehindotherenablerssuchasleadershipsupport.Toolsmaybeavailable,buteverydaybusiness

usersaren’talwaysempowered,trained,ormotivatedtousethemeffectively.

Therealityisthatmanyorganizationsstill

donothaveongoingliteracyandsupportprogramstailoredfornon-technicalusers.Additionally,itmaybethatsomebusinessusersdon’twanttousetoolsforself-

service;theymaynotseetheneed,ortheydon’tfeelthetoolsareeasyenoughtouse.

1See

/pages/assessments/bi-all-bi-and-ai-maturity-model-

assessment.aspx

》resultsofthisassessmentarecurrentlyunpublished.

StateofSelf-ServiceandAutomation7

Withouthands-ontraining,onboarding,andsupport,businessusersmayfeel

intimidatedorunqualifiedtousetools

beyondspreadsheets.Giventhis,itmakes

sensefororganizationstotrytoimplement

dataliteracyprograms.Thesecanbe

targetedprogramsthatarepersona-driven(e.g.,tailoredtodifferentusergroups).

Formalchangemanagementprogramsmightalsobehelpful.

Ofcourse,vendorsarealsostartingtousegenerativeAIasafrontendforself-serviceanalyticsandsucheasy-to-usefrontends

mayhelpincreaseadoption.OrganizationsarealsotryingtousegenerativeAIforself-service.Thefirstphaseofthisappearstobeanalyzingunstructureddocumentssuchascallcenternotesorincidentreports.ThesearefedintoagenerativeAIsystem,and

userscanaskthesystemtoclassifythekindsofissuesfound,forinstance.

However,tousegenerativeAIagainst

traditionalstructureddata,thekindthatisoftenusedforBI(oreventooperationalizetheunstructuredusecases),willrequirea

strongerinfrastructure.AndevenifbusinessusersareutilizinggenerativeAIfrontends,theseuserswillstillneedtobedata-literatetoasktherightquestionsintherightwayandeffectivelyevaluatetheresults.

TheStateofDataMaturity

forSelf-Service

Organizationalcultureisclearlyimportantforself-servicematurity,butsuccess

requiresmore.Thedatainfrastructureneedstobeinplace.Usersneedtobeabletoingest,find,andconsumedata

foranalytics.Thedataneedstobeeasilyaccessibleandeasytounderstand.It

can’tbefragmentedthroughoutthe

organization.Inotherwords,data

managementandinfrastructurecan’tbeahindrance.

Surveyresultssuggestthatorganizations

are,onaverage,operatingatamoderatelevelofcapabilityacrossseveralimportantareasofdatamaturity.Respondents’

maturityscoresaveragedfrom3to3.7

acrossquestionsonautomateddata

integrationpipelines,dataqualitypractices,metadatamanagement,andtrustindata.Thelowestscoringquestionwasabout

theuseofametadata-drivendatacatalog,whichscoreda3,andthehighestscoringquestionwasaboutdatatrust(3.7).Also

highwerematurityscoresaboutusing

automateddataintegrationpipelines(3.6).

Automateddataintegrationpipelines

playanimportantroleindelivering

timely,consistent,andreliabledatafromvarioussourcesystemstodownstream

users.Thesepipelinescaneliminate

manualdatahandling,reduceerrors,andsupportnear-real-timeupdates,which

areespeciallyimportantinfast-paced

businessenvironments.Automationalsofreesuptechnicalresources,allowingdataengineersandITtofocusonoptimizationandgovernanceratherthanrepetitiveETLtasks.Whenthesepipelinesarerobust

andwell-managed,theyenablebusinessuserstoaccesscurrentandrelevant

datafordecision-making,whichisakeyfoundationalrequirementforanyself-serviceanalyticsinitiative.

StateofSelf-ServiceandAutomation8

Similarly,dataqualityprocessesensurethat

thedatabeingusedisaccurate,complete,consistent,andreliable.Poordataqualitycanleadtoflawedanalysesandincorrect

decisions—theoldgarbagein,garbageoutissue.Poordataqualitycanalsoultimatelyleadtoalackofconfidenceinself-serviceplatformsandadecreaseinusage.Inthissurvey,theuseofautomatedtoolsfordataqualitymanagementsuchasmonitoring

andobservabilitytoolswasnotyetwidespread(Figure4).

Organizationsreporting

highermaturityindataqualitytoolsalsotendtoreport

highertrustintheirdata.

However,thereappearstobeastrong

conceptuallinkbetweendataquality

practicesandtrustinthedata.Whiletheseareaskedasseparateitemsinthesurvey,

organizationsreportinghighermaturityin

dataqualitytoolsalsotendtoreporthighertrustintheirdata.Itispossiblethatwhen

usersbelieveintheintegrityoftheirdata,

theyaremorelikelytoengageinself-serviceanalytics.Wehaveanecdotalevidencethatoncetrustisbreached,individualsareless

likelytobelieveintheirdata.

Asmentioned,withtheadventofgenerativeAI,weseeorganizationswantingtoanalyzetheirunstructureddata(suchascallcenternotesortroubletickets)aswellastheir

structureddata.However,accordingto

TDWIsurveys,organizationslikelytrusttheirunstructureddatalessthantheirstructured

data.2Theyarenotusedtomanagingthisdatainawaythatsupportsanalytics.Theremaybedifferentdataqualitymetricsfor

unstructureddatathanstructureddata.

Forinstance,plausibilitymightbeanew

metricinunstructureddocuments.Likewise,metricssuchasaccuracymightmean

somethingdifferentinunstructuredtextdocumentsthaninstructureddatabases.

Thatmeansorganizationswillneedto

create,redefine,andrefinecertainmetrics.

Yetredefiningdata-qualitymetricsdoeslittlegoodunlessthosedefinitions,andthedatatheyqualify,aresystematicallycaptured,

cataloged,andgoverned.Thistiesclosely

withmetadatamanagement,whichprovidesuserswithcontextaboutthedata—whatit

means,howitwascollected,whenitwaslastupdated,andwhoisresponsibleforit.

Metadatasystems,suchasdatacatalogs

(whichscoredlowinaveragematurityhere),improvedatadiscoverabilityandhelp

withtransparency,allowinguserstomakeinformeddecisionsaboutdatause.Theyprovidevisibilitytothedata.ThisisinlinewithwhatTDWIhasseeninothersurveys;datacatalogsarebecomingmoreofa

priorityfororganizations.Iforganizationscannotfindandusedataforself-service

easily,thentheymostlikelywon’tadoptit.

TheStateofDataInfrastructureMaturityforSelf-Service

Asoliddatainfrastructureiscriticalforenablingself-serviceanalytics.Itensuresfast,scalableaccesstotrusteddata,

2Unpublished2025TDWIDataandAnalyticssurvey.

StateofSelf-ServiceandAutomation9

Doesyourorganizationhaveprocessesandtoolsinplacetoautomaticallydetect,correct,andmaintainhighdataqualityacrossallcriticaldatasources?

No,andwehavenoplanstoimplementthesetools

9%

Notyet,butitisonourradarforthisyear

26%

Weareintheprocessofimplementingthesenow

30%

Thesetoolsareinuseacrossourdataenvironment,andsomeareautomated

22%

Thesetoolsareinuseacrossourdata

environment,theyareautomated,andwegetreportsaboutthehealthofourdata

13%

Figure4.Basedon215respondents.

supportsdiversedatatypes(structuredandunstructured),andintegratesautomationtostreamlineanalyticsprocesses.Moderninfrastructurealsoplaysacriticalrolein

reducingITbottlenecks,empowering

businessusersacrossdepartmentsto

performtheirownanalysesandgenerateinsightsindependently.Weaskedabout

scalabilityandperformance,datadiversity,andautomationformodernization,and

userenablementandaccessibility.

Inthesurveyforthisreport,theinfrastructurecapabilitywiththehighestaveragematurityscorewastheabilitytosupportfast,scalableself-serviceaccessthroughmodernplatformssuchascloudandhybridenvironments,

withascoreof3.6.Incontrast,thelowest-scoringareawastheextenttowhich

businessusersbeyonddataanalysts,suchasthoseinoperations,marketing,orfinance,canindependentlyaccessandanalyze

datawithoutsignificantITsupport.This

capabilityreceivedanaveragescoreof3.2,suggestingthatwhileorganizationsmay

havethetechnicalinfrastructureinplace,broaderuserenablementremainsakey

areaforimprovement.Thisisanareathatrespondentsstatethattheyareworkingonnow,with38%statingthattheyaremovingtowardsenablingbusinessusersbeyond

dataanalyststoindependentlyaccessandanalyzedatawithoutsignificantITsupport(Figure5).

Toenablebusinessusersbeyonddata

analyststoindependentlyaccessdata,

organizationsshouldprovideintuitive,role-baseddataaccessmechanismsthatensureuserscaneasilyfind,understand,and

retrievethedatatheyneedwithouttechnicalbarriers.Thisincludesimplementingwell-

governeddatacatalogs,standardizeddatadefinitions,andintegrationwithbusinessapplicationstousedata.

StateofSelf-ServiceandAutomation10

Automatedtoolsisanotherareathatis

importantinthedatainfrastructure.Given

thecomplexityoftheinfrastructure,with

manydiversedatatypes,itisimportant

tobeabletoautomatesomedata

managementprocessessuchasthepipelineprocessormetadatamapping.Forty-five

percentofrespondentstothesurvey(Figure6)indicatedthattheirenvironmentleveragesautomationandmoderntechnologiessuchassmartdatapipelinesandcatalog-drivenaccessintheirinfrastructure.

Itmakessensethatorganizationsare

integratingautomationintotheirbroader

dataoperationsandbusinessprocesses

tosupportself-serviceinitiatives.These

capabilitiesenablefaster,moreaccurate

updatestoenterprisesystemsandreducetheerrorsassociatedwithmanual,repetitivedatatasks.IncomplexERPenvironments,

forinstance,automationcansignificantly

reducemanualdataentryandmaintenancebyenablingstructured,repeatable

workflowsformassupdatestomaster

Ourinfrastructure(e.g.,cloudplatforms,hybridenvironments,datafabrics)supportsfast,scalableself-servicedataaccessandanalyticsacrossthe

organization.

Stronglydisagree4%

Disagree

Neutral

Agree

12%

25%

38%

Stronglyagree21%

Canbusinessusersbeyonddataanalysts(e.g.,operations,marketing,finance)independentlyaccessandanalyzedatawithoutsignificantITsupport?

No,notatall12%

InsomecasestheyareusingExcelspreadsheetswithgenerativeAI

Wearemovinginthatdirectionnow38%

Thishasbeenimplemented,althoughweworryaboutguardrails

20%

13%

Thishasbeenimplemented,andisgovernedmm17%

Figure5.Basedon215responses.

StateofSelf-ServiceandAutomation11

andtransactionaldata.Theseautomation

solutionscanprovideintuitiveinterfacesthatallowbusinessuserstoinitiateandmanagedatachanges,suchasonboardingnew

suppliers,updatingmaterialsinformation,ormodifyingcustomerrecords.

TheStateofSelf-ServiceEnablement

Forself-servicetosucceed,users,

especiallynon-technicalones,needto

beequippedtoperformanalytics.This

includesaccesstoavarietyoftools,

includinglow-code/no-codeplatformsforautomatingandmanagingdataprocessesandgenerativeAIfrontendsfornatural

languageexploration,togetherwith

trainingandsupportprogramsandthe

abilitytoperformmoreadvancedanalyticstasks.Theaveragematurityscoresacross

thesesurveyquestionsrangedfrom3to

3.3,indicatingthatwhileprogressisbeingmade,thereisstillroomforimprovementinhowusersaresupportedandempowered.

Thehighest-scoringpracticeinthis

categoryconcernedtrainingandsupporttoenablenon-technicaluserstoperformself-serviceanalytics.Thisquestionreceived

anaveragescoreof3.3,suggestingthatorganizationsrecognizetheimportanceofuserenablementandhavebegun

implementingtraining.However,ascoreinthelowthreesstillpointstomoderatematurity,whereconsistencyanddepthof

Ourenvironmentleveragesautomationandmoderntechnologies(e.g.,smartdatapipelines,catalog-drivenaccess)tomakeself-servicefastandreliable.

Stronglydisagree

6%

Disagree

24%

Neutral

25%

Agree

27%

Stronglyagree

18%

Figure6.Basedon215respondents.

StateofSelf-ServiceandAutomation12

trainingmayvaryacrossbusinessunitsorusergroups.Lessthan50%believethattheirtrainingenablesnon-technicaluserstobesuccessfu